SCNN: Scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT-DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes. 

/pdf/fingerprint-based-robust-medical-image-watermarking-in-1vtfpkhj.pdf

Fingerprint-based robust medical image watermarking in hybrid transform

With the advent of the technological era, the complexity of data security and privacy has gained utmost importance. People are becoming more conscious about the security and confidentiality of their crucial information. The present study is first of its kind effort in for safeguarding an individual’s electrocardiogram (ECG) data. ECG data not only does hold medical significance but also is of crucial and critical importance for purposes related to biometric information. Hence, a deciding factor for recognizing and validating personal information. The study progresses and proposes a new technique in ECG watermarking compression. This method ensures high reliability of data and information with confidentiality. The efficiency of the proposed research is evaluated in terms of compression ratio (CR), compressed signal-to-noise-ratio (CSNR), root-mean-square error (RMSE), maximum absolute error, percentage root-mean-square difference (PRD), and quality score. After a comprehensive evaluation, the final results show that the proposed work is robust and feasible.

A robust digital ECG signal watermarking and compression using biorthogonal wavelet transform

Design of a secured telehealth system based on multiple biosignals diagnosis and classification for IoT application

In order to improve the security of medical data, this paper uses a robust and secure dual watermarking technique in RDWT-SVD domain. Dual watermarking is achieved by embedding text watermark in medical watermark image using DWT, generating the final watermark. Before embedding, turbo code is used to encode the text watermark. Further, trade-off between imperceptibility and robustness is controlled in our scheme through firefly optimization. Experimental results indicate the proposed method offers high imperceptibility and robustness, and have superior overall performance.

RDWT-SVD-Firefly Based Dual Watermarking Technique for Medical Images (Workshop Paper)

Electrocardiogram (ECG) watermarking provides secure communication of patient information lies in a 1D- ECG signal. The primary challenge in ECG watermarking is the deterioration of an ECG signal which causes the loss and impotence to extract patient information. This paper proposes a wavelet method based watermarking scheme for patient information hiding in the ECG as a QR image. Here, we first convert the 1D-ECG signal to 2D-ECG image using the Pan–Tompkins algorithm. We use a wavelet transform to decompose 2D-ECG image. Wavelet analysis can capture the subtle underlying information of the ECG. Then we further decompose the detail coefficient of wavelet and the QR image using QR decomposition for embedding data. The embedding factor value calculation is adaptive by harnessing the entropy value of the signal. The hidden data is easily extractable with no distortion at the extractor side. The ECG data we use in this paper is from the MIT-BIH database. The results on this dataset suggest that our proposed approach is useful in patient information data hiding scheme in ECG. The proposed method outperforms the state-of-the-art.

Patient data hiding into ECG signal using watermarking in transform domain

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features.

Digital images are transferred with ease through the network. Many users are using the images without the knowledge of the owners. Therefore, a novel watermarking scheme is proposed to ensure copyright protection and authentication of images using cryptography techniques. Here, a quick response (QR) image is generated for a watermark image that contains public and private keys prepared using a cryptosystem. Later, this QR image is scrambled using a chaotic logistic map. The public and private keys are used to cipher and decipher the data. Next, the scrambled QR watermark is embedded into a color image using a single-level discrete wavelet transform followed by singular value decomposition using the key value. Finally, the inverse process is applied to extract the watermark. The proposed method is validated using various image processing attacks. The results are then compared with state-of-the-art watermarking schemes. The experimental results show that the scheme provides good results in terms of robustness and imperceptibility.

/pdf/digital-watermarking-system-for-copyright-protection-and-mrvzwoyo.pdf

Digital Watermarking System for Copyright Protection and Authentication of Images Using Cryptographic Techniques

In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method’s performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.

Kandala N. V. P. S. Rajesh

Papers

Patient data hiding into ECG signal using watermarking in transform domain

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features.

Digital Watermarking System for Copyright Protection and Authentication of Images Using Cryptographic Techniques

The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition

Automated Schizophrenia detection using local descriptors with EEG signals